1 Page 1-9 © MAT Journals 2018. All Rights Reserved Journal of Network Security Computer Networks e-ISSN: 2581-639X Volume 4 Issue 3 Data Aggregation Design Goals for Monitoring Data in Wireless Sensor Networks 1 Khushboo Jain, 2 Dr Anoop Bhola 1 PhD Research Scholar, 2 Assistant Professor Department of Computer Science Engineering, AIM & ACT Banasthali Vidyapith, Tonk, Rajasthan, India Email: 1 khushboojain2806@gmail.com, 2 anupbhola@banasthali.in DOI: Abstract Energy Constraint is the most significant issue in design of any wireless sensor network application. The communication between sensor nodes (SNs) is considered to be a major issue for fast energy drain. A crucial scheme to minimize energy utilization in WSN application is in-network data aggregation. It aims to reduce duplicate transmission of data frame by filtering the duplicate and unnecessary data values and thereby reducing the energy utilization. A recent trend in WSN proposes data accuracy and data latency as essential factors for various applications. Reducing data latency helps to enhance the network lifetime and also in detection of early events. Every SN has to wait for a predefined (which can be fixed or variable) time interval known as waiting time (WT) before performing aggregation function, in order to collect readings from other SNs. Data latency will be reduced and data accuracy will be increased if all SNs are well planned by a most favorable allocation of WT. Several solutions have been proposed for routing and aggregating data in WSN in order to maximize network lifetime and throughput. This study presents the classification of data aggregation design goals. Moreover we have analyzed each goal over it proficiency like data accuracy, data latency and energy utilization. Keywords: Wireless sensor network, Routing mechanism, Data aggregation function, Data aggregation scheduling, Data monitoring etc. INTRODUCTION With recent advances in wireless communications, WSNs have been regarded as an emerging and promising field in both academia and industry. It consists of randomly deployed sensor nodes (SNs) with the aim to gather data from the environment and send it to BS. Data is collected in a hop by hop fashion from the sensor nodes to the BS which acts as a database [1]. WSNs are deployed with distinctive properties of self-organization and self-deployment. SNs are resource constraints with energy, network capacity and processing capabilities. WSN main drawback is energy limitation which is dependent upon efficient aggregation function, aggregation schedule, routing algorithm and number of frames that is forwarded to BS. Data aggregation is a fundamental solution to minimize overheads and to optimize these constraints. Despite of sending all the readings from the sensor nodes separately, the data aggregations processes the data by intermediate nodes and only transmit reduced number of data frames to the BS. Data aggregation filters the duplicate and unnecessary data values and thus reduces the energy and capacity utilization by minimize redundant data frame transmissions. The operation of sensing in WSN majorly follows two different approaches. In the very first approach the sensing operation is